Commit Graph

10 Commits

Author SHA1 Message Date
45341a0bc8 feat(curator): switch Hermes Curator to DeepSeek V4-Pro via deepseek_local adapter
A/B test (2026-05-05) showed DeepSeek V4-Pro is 2-3x faster and ~20x cheaper
than Sonnet for style/lexicon pattern analysis, with comparable quality.
Adds adapters/deepseek-paperclip-adapter/ package, documents adapter requirements
(env injection, run-id headers), updates CLAUDE.md with adapter integration notes,
and records lessons from ערר 1200-25 (block order for 1xxx, "להלן מתוך" pattern,
expanded factual background, bridge planning analysis, flat heading structure).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-10 05:58:52 +00:00
1b14e04373 chore(skills): remove paperclip-dev, scope converting-plans-to-tasks
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 7s
paperclip-dev is for maintaining the Paperclip codebase itself — not
relevant to legal work. Removed from all 14 agents (was on CMPA mirror).

paperclip-converting-plans-to-tasks helps decompose a plan into assigned
issues. Useful for the planning-heavy agents (CEO, analyst). Now scoped
to those two — removed from the other 5 in CMPA where it had crept in.

Net effect: zero drift on paperclipai/* skills across all 7 master+mirror
pairs. Verified via the new Agents tab dashboard.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-04 17:47:05 +00:00
cf5f6fe274 feat(paperclip): close 11 integration gaps (#16-#28)
Brings the legal-ai ↔ Paperclip integration in line with the official
Paperclip skill. Net effect: HEARTBEAT.md -47% (370→195 lines), all 14
agents on uniform runtime_config + budget + instructionsBundleMode, and
two cross-company helpers replacing manual SQL.

Highlights:
- HEARTBEAT.md refactor: project-specific only, delegates to the official
  paperclipai/paperclip skill (loaded per agent). Adds heartbeat-context
  fast-path (§1.7) and PAPERCLIP_WAKE_PAYLOAD_JSON shortcut (§1.5).
- Issue Thread Interactions API: legal-ceo.md now uses
  ask_user_questions / request_confirmation / suggest_tasks instead of
  free-text comments — gives chair structured UI with idempotency keys.
- pc.sh + paperclip_api.pc_request: every API call goes through helpers
  that inject Authorization + X-Paperclip-Run-Id (audit trail).
- sync_agents_across_companies.py: master(CMP)→mirror(CMPA) sync via
  Paperclip API, idempotent, with --verify and --apply modes.
- skills/new-company-setup: 11-step blueprint distilling all 11 gaps
  into a single onboarding runbook for the next company.
- .taskmaster: 12 tasks covering each gap (one already closed: #29).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-04 17:25:45 +00:00
81ccf3a888 feat(retrieval): track page_number on text chunks for multimodal hybrid boost
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 6m33s
The legacy chunker did not track which PDF page each chunk came from.
Stored chunks had page_number=NULL, which blocked the multimodal
hybrid retriever's text+image boost — it joins (chunk, image) on
(document_id, page_number) and the join could never fire.

This change:

- extractor.extract_text now returns (text, page_count, page_offsets);
  page_offsets[i] is the start char offset of page (i+1) in the joined
  text. None for non-PDFs.
- chunker.chunk_document accepts an optional page_offsets and tags
  each chunk with the page that contains its first character (uses
  the existing chunker logic; pages assigned post-hoc by content
  search to keep the diff minimal).
- processor.process_document and precedent_library.ingest_precedent
  forward page_offsets through the chunker. New uploads now carry
  accurate page_number on every chunk.
- Other extract_text callers (tools/documents, tools/workflow,
  web/app.py) updated to unpack the third element (ignored).
- scripts/backfill_chunk_pages.py: per-case retrofit. Re-extracts each
  PDF (re-OCRs via Google Vision if needed, ~$0.0015/page), computes
  page_offsets, and updates page_number on every chunk by content
  search. Idempotent; --force re-runs on already-tagged docs.

Forward-only would leave the 419 image embeddings backfilled on
cases 8174-24 + 8137-24 unable to boost their corresponding text
chunks. The retrofit script closes that gap (cost ~$0.60).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-03 19:49:41 +00:00
242f668319 feat(retrieval): add voyage-multimodal-3 page-image embeddings (feature flag)
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 1m50s
Stage C: per-page image embeddings via voyage-multimodal-3 + hybrid
text+image search. Off by default; enable with MULTIMODAL_ENABLED=true.

- Schema V9: document_image_embeddings + precedent_image_embeddings
  (vector(1024), page_number, image_thumbnail_path)
- extractor.render_pages_for_multimodal renders PDF pages at
  MULTIMODAL_DPI (144) for embedding + JPEG thumbnails at
  MULTIMODAL_THUMB_DPI (96) for UI preview, in one pass
- embeddings.embed_images calls voyage-multimodal-3 in 50-page batches
- services/hybrid_search.py orchestrator: rerank applied to text side
  first (rerank-2 is text-only); image side cosine; weighted merge
  with text_weight 0.65 (env-tunable); image-only pages surface as
  match_type='image' so dense scanned content still appears
- processor.process_document and precedent_library.ingest_precedent
  gated by flag — non-fatal on multimodal failure
- scripts/multimodal_backfill.py — idempotent per-case CLI to embed
  existing documents without re-extracting text

Validated locally on a 5-page response brief: render 0.31s, embed 8.32s,
hybrid merge surfaces image rows correctly. Production rollout starts
with flag=false (no behavior change), then per-case A/B.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-03 19:24:52 +00:00
26c3fddf41 feat(retrieval): add voyage rerank-2 cross-encoder stage (feature flag)
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 1m29s
Stage B of voyage-upgrades-plan rewritten: instead of context-3 (which
4 POCs showed inconsistent improvement), add a cross-encoder rerank
layer on top of voyage-3. Default off (VOYAGE_RERANK_ENABLED=false).

POC validation (785-doc corpus, 12 queries, claude-haiku-4-5 judge):
- mean@3 +4.5% (4.306 → 4.500)
- practical-category queries +11.6% (3.78 → 4.22)
- latency +702ms per query
- no schema change, no re-embed, no double storage

Plumbing:
- config: VOYAGE_RERANK_ENABLED / _MODEL / _FETCH_K env vars
- embeddings.voyage_rerank() wraps voyageai client.rerank
- services/rerank.py: maybe_rerank() helper — fetches FETCH_K candidates
  via the bi-encoder then reranks to top-K. Fail-open if Voyage rerank is
  unavailable.
- tools/search.py: search_decisions, search_case_documents,
  find_similar_cases all wrapped
- services/precedent_library.search_library wrapped

Smoke-tested locally with flag on/off — produces expected behaviour and
latency profile. Ready for production rollout via Coolify env flip after
deploy.

POCs (kept under scripts/ for reference):
- voyage_context3_poc{_long}.py — context-3 evaluation (rejected)
- voyage_multimodal_poc.py — multimodal-3 (stage C, deferred)
- voyage_rerank_judge_poc.py — single-case rerank benchmark
- voyage_rerank_corpus_poc.py — full-corpus rerank validation

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-03 18:43:41 +00:00
da0a385d9c docs: register reembed_voyage.py in SCRIPTS.md
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 7s
2026-05-03 16:44:07 +00:00
28f49defff LLM session: async, 30min timeout, semantic chunking + parallel
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 1m28s
The claude_session bridge had two structural defects that made any
non-trivial document extraction unreliable:

  1. subprocess.run() blocks the asyncio event loop in the MCP server
     for the full duration of every LLM call (60-180s typical).
  2. The 120-second timeout was below the cold-cache cost of any
     document over ~12K Hebrew characters. Three back-to-back timeouts
     on case 8174-24 dropped 43 appellant claims on the floor.

Phase 1 of the remediation plan — keeps claude_session as the engine
(no Anthropic API switch) and restructures around it:

claude_session.py
  • query / query_json are now async — asyncio.create_subprocess_exec
    instead of subprocess.run, so MCP server can serve other coroutines
    while a call is in flight.
  • DEFAULT_TIMEOUT 120 → 1800 (30 min). High enough that no realistic
    document hits it; bounded so a runaway never zombifies forever.
  • LONG_TIMEOUT 300 → 3600 for opus block writing on full case context.
  • TimeoutError now actually kills the subprocess (asyncio.wait_for
    cancellation alone leaves the child running).

claims_extractor.py
  • _split_by_sections: chunks at numbered sections / Hebrew letter
    headings / "פרק" markers / markdown ##, falls back to paragraph
    breaks, then to hard splits. Targets 12K chars per chunk — small
    enough that each chunk reliably finishes inside the timeout.
  • _extract_chunk: per-chunk retry (1 attempt by default) with
    structured logging on failure. Failed chunks no longer crash the
    overall extraction; they're skipped with a partial-result warning.
  • extract_claims_with_ai now runs chunks in parallel via
    asyncio.gather bounded by a semaphore (CHUNK_CONCURRENCY=3).
    For a 25K-char appeal: was sequential 150-300s, now ~70-90s.

Updated all 9 callers (claims, appraiser facts, block writer, qa
validator, brainstorm, learning loop, style analyzer × 3) to await
the now-async API.

The one-shot scripts/extract_claims_8174.py used to recover 43
appellant claims on case 8174-24 has been moved to .archive/ — phase 1
makes it obsolete. SCRIPTS.md updated.

Phase 2 (background-task wrapper around LLM-bound MCP tools, persistent
llm_tasks table, SSE progress) is the structural follow-up — separate PR.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 14:21:35 +00:00
726498126d Add Track Changes architecture for draft revisions (CMP + CMPA)
All checks were successful
Build & Deploy / build-and-deploy (push) Successful in 1m29s
Fixes critical bug in 1033-25: user-uploaded עריכה-*.docx files were
orphaned on disk while exports kept rebuilding from stale DB blocks.

New architecture:
- User-uploaded DOCX becomes the source of truth (cases.active_draft_path)
- System edits via XML surgery with real Word <w:ins>/<w:del> revisions
- User can Accept/Reject each change from within Word

Components:
- docx_reviser.py: XML surgery for Track Changes (15 tests)
- docx_retrofit.py: retroactive bookmark injection with Hebrew marker
  detection + heading heuristic (9 tests)
- docx_exporter.py: emits bookmarks around each of the 12 blocks
- 3 new MCP tools: apply_user_edit, list_bookmarks, revise_draft
- 4 new/updated endpoints: upload (auto-registers active draft),
  /exports/revise, /exports/bookmarks, /exports/{filename}/retrofit,
  /active-draft
- DB migration: cases.active_draft_path column
- UI: correct banner using real v-numbers, "מקור האמת" badge,
  detailed upload toast with bookmarks_added/missing_blocks
- agents: legal-exporter (3 export modes), legal-ceo (stage G for
  revision handling), legal-writer (revision mode)

Multi-tenancy:
- Works for both CMP (1xxx cases) and CMPA (8xxx/9xxx cases)
- New revise-draft skill added to both companies
- deploy-track-changes.sh syncs skills CMP ↔ CMPA
- retrofit_case.py: one-off retrofit of existing files

Tests: 34 passing (15 reviser + 9 retrofit + 4 exporter bookmarks + 6 e2e)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-16 18:49:30 +00:00
5c9a5d702a Clean up scripts/: archive 17, delete 5, add SCRIPTS.md registry
Active scripts (5): auto-sync-cases.sh, backup-db.sh, restore-db.sh,
notify.py, bidi_table.py

Archived (17): one-time migration/seeding scripts whose functionality
is now in MCP server or web API. Moved to scripts/.archive/

Deleted (5): zero-value scripts (duplicates, hardcoded single-case,
debug scripts)

Added scripts/SCRIPTS.md — registry of all scripts with purpose,
status, and what superseded them. CLAUDE.md updated with rule:
any script change requires SCRIPTS.md update.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-14 16:30:19 +00:00